语言大模型重塑外语翻转课堂：个性化学习实现路径研究

Large Language Models Reshaping the Flipped Foreign Language Classroom: A Study on the Implementation Pathways of Personalized Learning

摘要: 教育信息化浪潮下，翻转课堂已成为外语教学改革的重要范式，但其在满足学生个性化学习需求方面存在显著局限。以GPT-4、DeepSeek等为代表的大型语言模型凭借其卓越的自然语言生成与交互能力，为破解这一难题提供了关键技术路径。本文系统梳理了国内外翻转课堂与语言大模型融合的研究现状，指出现有研究在个性化机制、教师角色转型及评价体系等方面的不足。基于建构主义与产出导向法(POA)理论，本文提出构建了一个融合语言大模型的外语个性化翻转课堂教学模式，并通过一学期的准实验研究，在一定程度上验证了其有效性。研究旨在构建一个融合语言大模型的外语个性化翻转课堂教学模式，通过“课前个性化预习–课中互动式深度学习–课后智能拓展巩固”的流程重构，实现学习资源与学习路径的精准适配。论文详细阐述了该模式的理论基础、设计原则、底层技术架构、实施框架及评估方法，并重点探讨了数据隐私、算法偏见等伦理风险的前置规避机制与技术整合的挑战。

Abstract: Amid the global trend of educational informatization, the flipped classroom has emerged as a key paradigm in the reform of foreign language teaching. However, it exhibits significant limitations in addressing students’ diverse and personalized learning needs. Large language models such as GPT-4 and ERNIE Bot, known for their advanced natural language generation and interactive capabilities, offer a promising technological solution to this challenge. This paper provides a systematic review of the current research on integrating flipped classrooms with large language models both domestically and internationally, highlighting existing deficiencies in personalization mechanisms, the transformation of teacher roles, and evaluation systems. Based on the theories of Constructivism and the Production-Oriented Approach (POA), this paper proposes the construction of a personalized flipped classroom model for foreign language teaching that integrates large language models. Through a one-semester quasi-experimental study, its effectiveness has been verified to a certain extent. By restructuring the instructional process into three phases—“personalized pre-class preparation,” “interactive in-class deep learning,” and “intelligent post-class consolidation and expansion”—the model seeks to achieve precise alignment between learning resources and individual learning pathways. This paper elaborates on the theoretical foundation, design principles, underlying technical architecture, implementation framework, and assessment methods of the model, with a particular focus on pre-emptive mechanisms for mitigating ethical risks such as data privacy and algorithmic bias, as well as the challenges of technological integration.